Diagnostic MicroRNA Signatures to Support Classification of Pulmonary Hypertension

Area of Science:

• Biomarker discovery
• Pulmonary Hypertension Research
• MicroRNA analysis

Background:

• Pulmonary hypertension (PH) classification is crucial for treatment but current biomarkers like NT-proBNP lack specificity for sub-groups.
• Existing diagnostic and risk stratification methods for PH are limited.
• There is a need for novel biomarkers to accurately diagnose and sub-classify PH patients.

Purpose of the Study:

• To identify and validate circulating microRNA (miRNA) signatures for diagnosing and sub-classifying pulmonary hypertension (PH).
• To compare the performance of miRNA signatures against NT-proBNP for PH detection and classification.
• To explore the combined utility of miRNA signatures with clinical factors for improved PH sub-classification.

Main Methods:

• Serum samples from 1150 PH patients and 334 controls were analyzed for 650 miRNAs.
• Machine learning models prioritized NT-proBNP and 326 miRNAs.
• Generalized linear models identified miRNA signatures for differentiating PH and its subtypes, validated in independent cohorts.

Main Results:

• NT-proBNP showed moderate accuracy in identifying PH but could not sub-classify.
• miRNA signatures performed comparably to NT-proBNP in detecting PH but outperformed it in sub-classification.
• A combination of miRNA signatures, NT-proBNP, age, and sex demonstrated superior performance in PH sub-classification.

Conclusions:

• NT-proBNP can serve as an initial screening tool for PH.
• Circulating miRNA signatures offer a promising approach for accurate PH sub-classification.
• Integrating miRNA signatures with clinical data enhances the diagnostic and prognostic capabilities for PH patients.